Class 11 Computer Science · Chapter 3 · Test 2
Big O and Design
38 MCQs Covers time and space complexity, Big O notation (O(1), O(log n), O(n), O(n²)), efficiency and scalability.
38 questions · 35 min · 38 marks
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Sample questions
The first 5 of 38. Try each one, then tap to see the answer.
Question 1
What does time complexity measure?
- AThe amount of memory an algorithm uses
- BHow the runtime increases with input size
- CThe number of programmers needed
- DThe cost of the algorithm
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Answer: B
Time complexity shows how running time grows as the input grows.
Question 2
What does Big O notation describe?
- AThe smallest possible runtime
- BThe cost of hardware
- CThe number of programmers
- DThe upper bound on runtime as input size grows
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Answer: D
Big O gives the worst-case growth of an algorithm.
Question 3
Which of the following is an example of O(1) time complexity?
- AAdding two numbers
- BSearching a list
- CBubble sort
- DComparing every student with every other student
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Answer: A
Adding two numbers takes the same time whatever the input.
Question 4
What is space complexity?
- AThe time taken by an algorithm
- BThe number of programmers
- CThe amount of memory used by an algorithm
- DThe cost of the computer
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Answer: C
Space complexity measures memory use as input grows.
Question 5
Divide and Conquer works by:
- AMaking random guesses
- BBreaking a problem into smaller parts and solving each part
- CIgnoring the problem
- DUsing only one step
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Answer: B
The results of the parts are combined, as in Merge Sort.
More tests in Algorithms and Problem Solving
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